DESCRIPTION: Biology uses complex regulatory networks to sense and regulate cell state. Synthetic molecular circuits that can similarly control the timing and location of gene expression will have important applications in targeted disease therapy, cellular reprogramming, and beyond. However, a reliable, scalable, and general molecular technology for programmable gene expression has not yet been demonstrated. Here, we propose a paradigm shifting approach to this challenge: we will develop RNA strand displacement-based cellular bio computers. To date, strand displacement has primarily been demonstrated with DNA oligonucleotides in cell free settings. Strand displacement has been used effectively in cell-free DNA nanotechnology to build complex multi-input logic circuits, programmable nanostructures and molecular motors. Logic circuits made from hundreds of DNA oligonucleotides constitute the largest man-made molecular circuits built so far. In fact, there is currently no other engineering technology that supports de novo design of similarly complex, scalable and modular molecular circuitry, making this approach an intriguing candidate for performing biological computation in cells. Here we plan to bring strand displacement circuits to the cellular environment through the use of RNA instead of DNA, including sensors for endogenous RNA and RNA-based gene regulation. By foregoing the use of transcription factors and promoter regulation orthogonal with we can rapidly build more sophisticated circuits, cellular processes , which can be delivered more easily. We estimate that encoding of strand displacement circuits can be up to 10-fold more compact than genetic encoding of an equivalent circuit using transcriptional regulation. DNA serves as the information-storage medium, while transcribed RNA acts as the information- processing medium. Our RNA parts are engineered to interact with the cell milieu through specialized sensing and actuation components. We will demonstrate that, in principle, any endogenous cellular mRNA or miRNA can be an input, and that output gene expression can be regulated through RNA-RNA interactions. We will construct multi-input sensory circuits that provide high content information about cell state, and apply this for understanding biomarker levels and correlations for an in vitro and an in vivo 4T1 mouse breast cancer model. Importantly, our ability to encode highly sophisticated genetic programs with a much smaller DNA footprint will allow us to overcome current in vivo delivery limitations of complex circuitry. We initially focus on breast cancer as a model system but our technology can readily be adapted to other biomarkers, cancer types, and disease models. In fact, we believe that this adaptability, grounded in a rational design approach, is the key strength of the proposed technology. We expect that our technology will become relevant for many other applications that require sensing, analysis and control of cell state, including diagnostics and imaging applications, understanding of disease models, or programmed control of multi-stage differentiation.